Title: Uncertainty for deep image classifiers on out of distribution data. . Abstract: In addition to  achieving high accuracy, in many applications, it is important to  estimate the probability that a model prediction is correct. Predictive uncertainty is particularly important on out of distribution (OOD) data where accuracy degrades.  However, models are typically overconfident, and model calibration on OOD data remains a challenge. In this paper we propose a simple post hoc calibration method that significantly  improves on benchmark results [Ovadia et al 2019] on a wide range of corrupted data.  Our method uses outlier exposure to properly calibrate the model probabilities.