In photometric stereo a robust method is required to deal with outliers, such as shadows and non-Lambertian reflections. In this paper we rely on a probabilistic imaging model that distinguishes between inliers and outliers, and formulate the problem as a Maximum-Likelihood estimation problem. To signal which imaging model to use a hidden binary inlier map is introduced, which, to account for the fact that inlier/outlier pixels typically group together, is modelled as a Markov Random Field. To make inference of model parameters and hidden variables tractable a mean field Expectation-Maximization (EM) algorithm is used. If for each pixel we add the scaled normal, i.e. albedo and normal combined, to the model parameters, it would not be possible to obtain a confidence estimate in the result. Instead, each scaled normal is added as a hidden variable, the distribution of which, approximated by a Gaussian, is also estimated in the EM algorithm. The covariance matrix of the recovered approximate Gaussian distribution serves as a confidence estimate of the scaled normal. We demonstrate experimentally the effectiveness or our approach.