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While the accuracy of modern deep learning models has significantly improvedin recent years, the ability of these models to generate uncertainty estimateshas not progressed to the same degree. Uncertainty methods are designed toprovide an estimate of class probabilities when predicting class assignment.  While there are a number of proposed methods for estimating uncertainty, theyall suffer from a lack of calibration: predicted probabilities can be off fromempirical ones by a few percent or more. By restricting the scope of ourpredictions to only the probability of Top-1 error, we can decrease thecalibration error of existing methods to less than one percent. As a result,the scores of the methods also improve significantly over benchmarks.

Calibrated Top-1 Uncertainty estimates for classification by score based models