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Machine learning techniques like pointwise classification are widely used for object detection and segmentation. However, for large search spaces like CT images, this approach becomes computationally very demanding. Designing strong yet compact classifiers is thus of great importance for systems that ought to be clinically used as time is a limiting factor in clinical routine. The runtime of a system plays an important role in the decision about its application. In this paper we propose a novel technique for reducing the computational complexity of voxel classification systems based on the well-known AdaBoost algorithm in general and Probabilistic Boosting Trees in particular. We describe a means of incorporating a measure of hypothesis complexity into the optimization process, resulting in classifiers with lower evaluation cost. More specifically, in our approach the hypothesis generation that is performed during the AdaBoost training is no longer based only on the error of a hypothesis but also on its complexity. This leads to a reduced overall classifier complexity and thus shorter evaluation times. The validity of the approach is shown in an experimental evaluation. In a cross validation experiment, a system for automatic segmentation of liver tumors in CT images, that is based on the Probabilistic Boosting Tree, was trained with and without the proposed extension. In this preliminary study, the evaluation cost for classifying previously unseen samples could be reduced by 83% using the methods described here without losing classification accuracy.