diff --git a/ignite/metrics/ExpectedCalibrationError.py b/ignite/metrics/ExpectedCalibrationError.py
new file mode 100644
index 000000000000..5f338528c453
--- /dev/null
+++ b/ignite/metrics/ExpectedCalibrationError.py
@@ -0,0 +1,81 @@
+import torch
+
+from ignite.exceptions import NotComputableError
+from ignite.metrics import Metric
+
+
+class ExpectedCalibrationError(Metric):
+    def __init__(self, num_bins=10, device=None):
+        super(ExpectedCalibrationError, self).__init__()
+        self.num_bins = num_bins
+        self.device = device
+        self.reset()
+
+    def reset(self):
+        self.confidences = torch.tensor([], device=self.device)
+        self.corrects = torch.tensor([], device=self.device)
+
+    def update_binary(self, y_pred, y):
+        y_pred, y = y_pred.detach().unsqueeze(1), y.detach()
+
+        softmax_probs = torch.sigmoid(y_pred)
+        max_probs = softmax_probs.squeeze()
+        predicted_class = torch.round(max_probs)
+
+        self.confidences = torch.cat((self.confidences, max_probs))
+        self.corrects = torch.cat((self.corrects, predicted_class == y))
+
+    def update_multi_class(self, y_pred, y):
+        y_pred, y = y_pred.detach(), y.detach()
+
+        softmax_probs = torch.softmax(y_pred, dim=1)
+        max_probs, predicted_class = torch.max(softmax_probs, dim=1)
+
+        self.confidences = torch.cat((self.confidences, max_probs))
+        self.corrects = torch.cat((self.corrects, predicted_class == y))
+
+    def update(self, output):
+        y_pred, y = output
+
+        if y_pred.dim() == 2:
+            # Multi-class classification
+            if y_pred.shape[1] <= 1:
+                raise ValueError("Invalid number of classes for multi-class ECE computation.")
+
+            self.update_multi_class(y_pred, y)
+
+        elif y_pred.dim() == 1:
+            # Binary classification
+            self.update_binary(y_pred, y)
+
+        else:
+            raise ValueError("Invalid input dimensions for ECE computation.")
+
+    def compute(self):
+        if self.confidences.numel() == 0:
+            raise NotComputableError(
+                "ExpectedCalibrationError must have at least one example before it can be computed."
+            )
+
+        bin_edges = torch.linspace(0, 1, self.num_bins + 1, device=self.device)
+
+        bin_indices = torch.searchsorted(bin_edges, self.confidences)
+
+        ece = 0.0
+        bin_sizes = torch.zeros(self.num_bins, device=self.device)
+        bin_accuracies = torch.zeros(self.num_bins, device=self.device)
+
+        for i in range(self.num_bins):
+            mask = bin_indices == i
+            bin_confidences = self.confidences[mask]
+            bin_corrects = self.corrects[mask]
+
+            accuracy = torch.mean(bin_corrects)
+            avg_confidence = torch.mean(bin_confidences)
+
+            bin_size = bin_confidences.numel()
+            ece += (bin_size / len(self.confidences)) * abs(accuracy - avg_confidence)
+            bin_sizes[i] = bin_size
+            bin_accuracies[i] = accuracy
+
+        return ece