Predictive uncertainties from both DER and deep ensembles lacked proper calibration. To address this, we employed posthoc calibration techniques, utilizing both non-parametric methods (isotonic regression and standard scaling) and parametric methods (GPNormal). Isotonic Regression: Isotonic regression , also known as monotonic regression, is a method used to fit a free-form line to a sequence of observations while adhering to specific constraints: the fitted line must be non-decreasing (or non-increasing) throughout and should remain as close to the observed data as possible. One advantage of isotonic regression is its flexibility; it is not restricted by a predefined functional form, such as the linearity requirement in linear regression, as long as the function remains monotonically increasing. The isotonic regression task can be formulated as a quadratic programming (QP) problem