We present a probabilistic epileptic seizure predictor and an according method for its statistical evaluation. The probabilistic predictor is based on a combination of feature channels, which are derived from the intracranial electroencephalogram (EEG), by a logistic regression map. The evaluation is done by the Brier score, an established assessment method in meteorology, which quantifies the prediction error. From the prediction features, the weights of the logistic regression are learned in a training phase and in a test phase the Brier score is assessed. A test for significance of the probabilistic predictor, based on seizure time surrogates, is computed. For 3 of 5 patients we obtained significant predictive power with the mean phase coherence and with the dynamical similarity index we obtained for 2 of the 5 patients significant results. The concept of probabilistic prediction can be a valuable tool for the development of future seizure intervention systems.