Neural networks are complex statistical systems that are notoriously hard to interpret. When using neural networks in an automotive context, it is crucial to understand when and why a model struggles to predict a scene correctly. In our quest to continuously improve our models, uncertainty estimation is a centerpiece in understanding what data we need to collect to improve the predictions of our AI models. This deep dive will explain several methods that can be applied to estimate uncertainty of neural networks and discuss with the participants the advantages, pitfalls and lessons learned of different approaches.