Supervised segmentation methods in which a model of the shape of an object and its gray-level appearance is used to segment new images have become popular techniques in medical image segmentation. However, the results of these methods are not always accurate enough. We show how to extend one of these segmentation methods, active shape models (ASM) so that user interaction can be incorporated. In this interactive shape model (iASM), a user drags points to their correct position thus guiding the segmentation process. Experiments for three medical segmentation tasks are presented: segmenting lung fields in chest radiographs, hand outlines in hand radiographs and thrombus in abdominal aorta aneurysms from CTA data. By only fixing a small number of points, the part of sufficiently accurate segmentations can be increased from 20-70% for no interaction to over 95%. We believe that iASM can be used in many clinical applications.