The paper investigates the use of an ensemble of neural networks (each modelling the probability of the target given the input and trained with adversarial training) for quantifying predictive uncertainty. A series of experiments shows that this simple approach archives competitive results to the standard Bayesian models. While the proposed approach is based on well-known models and techniques (and thus is not new itself), to the best of my knowledge it has not been applied to the problem of estimating predictive uncertainty so far and could serve as a good benchmark in future. A drawback compared to the Bayesian models is that the approach comes without mathematical framework and guarantees. Specific comments and questions: - While other approaches, like MC-dropout can also be applied to regression problems with multidimensional targets, it is not clear to me, if a training criterion (like that described in section 2.2.1) suitable for multidimensional targets does also exists (learning a multivariate Gaussian with dependent variables seems not state forward).