  The role of uncertainty quantification (UQ) in deep learning has become
crucial with growing use of predictive models in high-risk applications. Though
a large class of methods exists for measuring deep uncertainties, in practice,
the resulting estimates are found to be poorly calibrated, thus making it
challenging to translate them into actionable insights. A common workaround is
to utilize a separate recalibration step, which adjusts the estimates to
compensate for the miscalibration. Instead, we propose to repurpose the
heteroscedastic regression objective as a surrogate for calibration and enable
any existing uncertainty estimator to be inherently calibrated. In addition to
eliminating the need for recalibration, this also regularizes the training
process. Using regression experiments, we demonstrate the effectiveness of the
proposed heteroscedastic calibration with two popular uncertainty estimators.
