diff --git a/monai/handlers/segmentation_saver.py b/monai/handlers/segmentation_saver.py
index c3eca3b9c8..c04f9e1701 100644
--- a/monai/handlers/segmentation_saver.py
+++ b/monai/handlers/segmentation_saver.py
@@ -10,7 +10,6 @@
 # limitations under the License.
 
 import logging
-import warnings
 from typing import TYPE_CHECKING, Callable, Optional, Union
 
 import numpy as np
@@ -121,7 +120,6 @@ def __init__(
             squeeze_end_dims=squeeze_end_dims,
             data_root_dir=data_root_dir,
         )
-        self.resample = resample
         self.batch_transform = batch_transform
         self.output_transform = output_transform
 
@@ -150,12 +148,15 @@ def __call__(self, engine: Engine) -> None:
         engine_output = self.output_transform(engine.state.output)
         if isinstance(engine_output, (tuple, list)):
             # if a list of data in shape: [channel, H, W, [D]], save every item separately
-            if self.resample:
-                warnings.warn("if saving inverted data, please set `resample=False` as it's already resampled.")
-
             self._saver.save_batch = False
             for i, d in enumerate(engine_output):
-                self._saver(d, {k: meta_data[k][i] for k in meta_data} if meta_data is not None else None)
+                if isinstance(meta_data, dict):
+                    meta_ = {k: meta_data[k][i] for k in meta_data}
+                elif isinstance(meta_data, (list, tuple)):
+                    meta_ = meta_data[i]
+                else:
+                    meta_ = meta_data
+                self._saver(d, meta_)
         else:
             # if the data is in shape: [batch, channel, H, W, [D]]
             self._saver.save_batch = True
diff --git a/monai/handlers/transform_inverter.py b/monai/handlers/transform_inverter.py
index 5f5c141189..651a47c39c 100644
--- a/monai/handlers/transform_inverter.py
+++ b/monai/handlers/transform_inverter.py
@@ -33,7 +33,10 @@ class TransformInverter:
     """
     Ignite handler to automatically invert `transforms`.
     It takes `engine.state.output` as the input data and uses the transforms information from `engine.state.batch`.
-    The inverted results are stored in `engine.state.output` with key: "{output_key}_{postfix}".
+    The inverted data are stored in `engine.state.output` with key: "{output_key}_{postfix}".
+    And the inverted meta dict will be stored in `engine.state.batch`
+    with key: "{output_key}_{postfix}_{meta_key_postfix}".
+
     """
 
     def __init__(
@@ -136,8 +139,14 @@ def __call__(self, engine: Engine) -> None:
                 segs_dict[meta_dict_key] = engine.state.batch[meta_dict_key]
 
             with allow_missing_keys_mode(self.transform):  # type: ignore
-                inverted_key = f"{output_key}_{self.postfix}"
-                engine.state.output[inverted_key] = [
-                    post_func(self._totensor(i[batch_key]).to(device) if to_tensor else i[batch_key])
-                    for i in self.inverter(segs_dict)
-                ]
+                inverted = self.inverter(segs_dict)
+
+            # save the inverted data into state.output
+            inverted_key = f"{output_key}_{self.postfix}"
+            engine.state.output[inverted_key] = [
+                post_func(self._totensor(i[batch_key]).to(device) if to_tensor else i[batch_key]) for i in inverted
+            ]
+
+            # save the inverted meta dict into state.batch
+            if meta_dict_key in engine.state.batch:
+                engine.state.batch[f"{inverted_key}_{self.meta_key_postfix}"] = [i.get(meta_dict_key) for i in inverted]