Several studies assessing the performance of different UQ methods exist, see e.g. . Such comparison studies are challenged by the fact that the true uncertainties are generally not available. Rather the UQ methods are evaluated based on a single error-observation for each predicted uncertainty. Since no obvious evaluation metric exists, different studies use different evaluation metrics. Three of the popular evaluation metrics are Spearman's rank correlation coefficient, the miscalibration area and the negative log likelihood (NLL). Some studies employing UQ estimates use all three evaluation metrics while some use one of them . However, as pointed out by Hirschfeld et al. in their benchmark study comparing a multitude of UQ methods on several datasets these three evaluation metrics do not necessarily agree on which UQ estimates are better.