Most machine learning (ML) and deep learning (DL) models can be regarded as black-box models providing predictions without including a notion of uncertainty associated to said predictions. Even for the task of classification, where the softmax output of a typical ML model can be interpreted as a measure of uncertainty of the prediction, the softmax values are usually highly overconfident and ill-calibrated. The lack of a proper uncertainty quantification in ML models is one of the key elements that hinders their adoption in critical applications. It is also a major obstacle to the associated certification process. Predictive Uncertainty Quantification (UQ) is the field of research that deals with quantifying the uncertainty in the predictions of neural networks and other ML models. It aims at building trustworthy ML models, by providing not only point-predictions, but also a notion of the uncertainty associated to these predictions. Based on these uncertainty estimates, users can safely decide when to trust and when not to trust the predictions of the model.