  Recent works have shown that deep neural networks can achieve super-human
performance in a wide range of image classification tasks in the medical
imaging domain. However, these works have primarily focused on classification
accuracy, ignoring the important role of uncertainty quantification.
Empirically, neural networks are often miscalibrated and overconfident in their
predictions. This miscalibration could be problematic in any automatic
decision-making system, but we focus on the medical field in which neural
network miscalibration has the potential to lead to significant treatment
errors. We propose a novel calibration approach that maintains the overall
classification accuracy while significantly improving model calibration. The
proposed approach is based on expected calibration error, which is a common
metric for quantifying miscalibration. Our approach can be easily integrated
into any classification task as an auxiliary loss term, thus not requiring an
explicit training round for calibration. We show that our approach reduces
calibration error significantly across various architectures and datasets.
