diff --git a/monai/metrics/metric.py b/monai/metrics/metric.py
index bb4aa7c343..f54bb984f0 100644
--- a/monai/metrics/metric.py
+++ b/monai/metrics/metric.py
@@ -120,7 +120,7 @@ class Cumulative(ABC):
         cum.add(a, b)
         cum.add(c, d)
         cum.aggregate()
-        result = cum.get_buffer()
+        result = cum.get_buffer()  # optional
         cum.reset()
 
     """
@@ -197,6 +197,27 @@ class CumulativeIterationMetric(Cumulative, IterationMetric):
     Typically, it computes some intermediate results for every iteration, cumulates in buffers,
     then syncs across all the distributed ranks and aggregates for the final result when epoch completed.
 
+    For example, `MeanDice` inherits this class and the usage:
+
+    .. code-block:: python
+
+        dice_metric = DiceMetric(include_background=True, reduction="mean")
+
+        for val_data in val_loader:
+            val_outputs = model(val_data["img"])
+            val_outputs = [postprocessing_transform(i) for i in decollate_batch(val_outputs)]
+            # compute metric for current iteration
+            dice_metric(y_pred=val_outputs, y=val_data["seg"])
+
+        # aggregate the final mean dice result
+        metric = dice_metric.aggregate().item()
+
+        # reset the status for next computation round
+        dice_metric.reset()
+
+    And to load `predictions` and `labels` from files, then compute metrics with multi-processing, please refer to:
+    https://github.com/Project-MONAI/tutorials/blob/master/modules/compute_metric.py.
+
     """
 
     def __call__(self, y_pred: TensorOrList, y: Optional[TensorOrList] = None):  # type: ignore