Over the last decade, Deep NeuralNetworks (DNNs) have become a popular choice to implement Learning-Enabled Components LECs in automated systems thanks to their effectiveness in processing complex sensory inputs, and their powerful representation learning that surpasses the performance of traditional methods. Despite the remarkable progress in representation learning, DNNs should also represent the confidence in their predictions to deploy them in safety-critical systems. Bayesian Neural Networks (BNNs) offer a principled framework to model and capture uncertainty in LECs. However, exact inference in BNNs is difficult to compute. Thus, we rely on sampling techniques to approximate the true posterior of the weights for computing the posterior predictive distribution (inference). In this regard, relatively simple though computationally and memory expensive sample-based methods have been pro posed for approximate Bayesian inference to quantify uncertainty in DNNs, e.g., Monte-Carlo dropout or Deep Ensembles. Efficient DNN uncertainty estimation in resource-constrained hardware platforms remains an open problem, limiting the adoption within applications from highly automated systems that possess strict computation and memory budgets, tight time constraints, and safety requirements. This thesis aims to develop novel methods and hardware optimizations for efficient and reliable uncertainty estimation in modern DNN architectures deployed in hardware platforms with limited computation resources.