Prior to radiation therapy planning, tumours and organs at risk need to be delineated. In recent years, deep learning models have opened the possibility of automating the contouring process, speeding up the procedures and helping clinicians. However, deep learning models, trained using ground truth labels from different clinicians, inevitably incorporate the human-based inter-observer variability as well as other machine-based uncertainties and biases. Consequently, this affects the accuracy of segmentation, representing the primary source of error in contouring tasks. Therefore, clinicians still need to check and manually correct the segmentation and still do not have a measure of reliability. To tackle these issues researchers have shifted their focus to the topic of probabilistic neural networks and uncertainties in deep learning models.