  Model uncertainty obtained by variational Bayesian inference with Monte Carlo
dropout is prone to miscalibration. The uncertainty does not represent the
model error well. In this paper, temperature scaling is extended to dropout
variational inference to calibrate model uncertainty. Expected uncertainty
calibration error (UCE) is presented as a metric to measure miscalibration of
uncertainty. The effectiveness of this approach is evaluated on CIFAR-10/100
for recent CNN architectures. Experimental results show, that temperature
scaling considerably reduces miscalibration by means of UCE and enables robust
rejection of uncertain predictions. The proposed approach can easily be derived
from frequentist temperature scaling and yields well-calibrated model
uncertainty. It is simple to implement and does not affect the model accuracy.
