Disparity and depth estimation of images is a fundamental problem for computer vision. Recent work has shown that convolutional neural networks are effective at both monocular and binocular depth prediction. However, standard neural networks do not give any information about the confidence of their predictions, making it impossible to know if a measurement could be inaccurate. In this work, we add Bayesian uncertainty to pretrained convolutional neural networks. Testing the networks on a synthetic dataset shows that the uncertainty is able to give confidence levels that are linked with the accuracy of the model output. Additionally, masking high uncertainty areas increases the remaining accuracy at the cost of decreasing the completeness of the output.