To be clear, calibration refers to the long-run accuracy of predicted probabilities.
a probabilistic classifier, h θ (x), is well calibrated at the level µ if, among all instances for which the classifier predicts class k with a probability of µ, the proportion that are truly assigned to class k is also equal to µ.