Interactive segmentation methods have greatly simplified the task of object cutout from an image. However, segmenting a large number of images in a collection is still a tedious task. In this paper, we present a content-sensitive group segmentation method that iteratively segments the images in a collection and incrementally refines the results. With our method, a user only provides a small number of strokes to segment and refine a few sample images. For each of the rest of images, our method finds relevant sample images and applies the corresponding appearance models to guide the segmentation. To improve the segmentation results using the user strokes on a few images with unsatisfactory segmentation results, our method calculates the relevance map that measures the probability that a stroke can be appropriately applied at each pixel/region of an image, and applies it accordingly. Our experiments show that our method can effectively segment an image collection with a wide variety of image content and significantly reduce user input.