Forming expectations about the outcome of an action is an important prerequisite for action control and reinforcement learning in the human brain. The medial frontal cortex (MFC) has been shown to play an important role in the representation of outcome expectations, particularly when an update of expected outcome becomes necessary because an error is detected. However, error detection alone is not always sufficient to compute expected outcome because errors can occur in various ways and different types of errors may be associated with different outcomes. In the present study, we therefore investigate whether updating expected outcome in the human MFC is based on an evaluation of error type. Our approach was to consider an electrophysiological correlate of MFC activity on errors, the error-related negativity (Ne/ERN), in a task in which two types of errors could occur. Because the two error types were associated with different amounts of monetary loss, updating expected outcomes on error trials required an evaluation of error type. Our data revealed a pattern of Ne/ERN amplitudes that closely mirrored the amount of monetary loss associated with each error type, suggesting that outcome expectations are updated based on an evaluation of error type. We propose that this is achieved by a proactive evaluation process that anticipates error types by continuously monitoring error sources or by dynamically representing possible response-outcome relations.