EDL represents an advanced methodology for encoding uncertainty within ML models by employing a specialized architecture. This approach introduces an output layer specifically tailored to parameterize the Normal-Inverse-Gamma distribution, an important feature that simultaneously captures the predicted mean and the associated variability, characterized by standard deviations. Opting for this distribution is strategic, as it permits a comprehensive encapsulation of uncertainty, encompassing a wide spectrum of variabilities in the predictions without resorting to the predetermined assumptions commonly associated with BNN. Unlike MVE, which primarily addresses uncertainty in the model's outputs, EDL is capable of distinguishing between aleatoric and epistemic uncertainties by employing a specially defined loss function.