An option to get better calibrated errors for the evidential epistemic uncertainties is to tune the hyperparameter, λ. λ is a parameter in the evidential loss function determining to what degree it should be prioritized that evidence is lowered for high-error points during training (see section S2 or Amini et al. for details on the evidential loss function). As an example, results for λ = 0.1 is presented in section S3, but for a more detailed analysis of the effect of λ for evidential models see . Another option is to re-calibrate the uncertainties post training. A popular approach for such re-calibration is a linear re-calibration where the linear parameters are determined by minimizing the NLL on the uncertainties+errors (under assumption of Gaussian errors) of the validation set. This approach was used for the latent space uncertainties and has been applied in several UQ studies . We propose another simple option, that does not assume Gaussian errors, which is to simply use the fitted slope and intercept from a RMSE/RMV plot of the validation set to re-calibrate the uncertainties according to: