The exploration-exploitation dilemma is still an open problem in Reinforcement Learning (RL), especially when coped with deep architectures and in the context of continuous action spaces. Uncertainty quantification has been extensively used as a means to achieve efficient directed exploration. However, state-of-the-art methods for continuous actions still suffer from high sample complexity requirements. Indeed, they either completely lack strategies for propagating the epistemic uncertainty throughout the updates, or they mix it with aleatory uncertainty while learning the full return distribution (e.g., distributional RL). In this paper, we propose Wasserstein Actor-Critic (WAC), an actor-critic architecture inspired by the recent Wasserstein Q-Learning (WQL) (Metelli et al., 2019), that employs approximate Q-posteriors to represent the epistemic uncertainty and Wasserstein barycenters for uncertainty propagation across the state-action space. WAC enforces exploration in a principled way by guiding the policy learning process with the optimization of an upper bound of the Q-value estimates. Furthermore, we study some peculiar issues that arise when using function approximation, coupled with the uncertainty estimation, and propose a regularized loss for the uncertainty estimation. Finally, we evaluate our algorithmon a suite of continuous-actions domains, where exploration is crucial, in comparison with state-of-the-art baselines. Our experiments show a clear benefit of using uncertainty-aware critics for continuous-actions control.