  Reliable uncertainty quantification in deep neural networks is very crucial
in safety-critical applications such as automated driving for trustworthy and
informed decision-making. Assessing the quality of uncertainty estimates is
challenging as ground truth for uncertainty estimates is not available.
Ideally, in a well-calibrated model, uncertainty estimates should perfectly
correlate with model error. We propose a novel error aligned uncertainty
optimization method and introduce a trainable loss function to guide the models
to yield good quality uncertainty estimates aligning with the model error. Our
approach targets continuous structured prediction and regression tasks, and is
evaluated on multiple datasets including a large-scale vehicle motion
prediction task involving real-world distributional shifts. We demonstrate that
our method improves average displacement error by 1.69% and 4.69%, and the
uncertainty correlation with model error by 17.22% and 19.13% as quantified by
Pearson correlation coefficient on two state-of-the-art baselines.
