Medical image segmentation is a crucial and time-consuming task in clinical care, where precision is extremely important. The Segment Anything Model (SAM) offers a promising approach, providing an interactive interface based on visual prompting and edition. However, this model and adaptations for medical images are built for 2D images, whereas a whole medical domain is based on 3D images, such as CT and MRI. This requires one prompt per slice, making the segmentation process tedious. We propose RadSAM, a novel method for segmenting 3D objects with a 2D model from a single prompt, based on an iterative inference pipeline to reconstruct the 3D mask slice-by-slice. We introduce a benchmark to evaluate the model’s ability to segment 3D objects in CT images from a single prompt and evaluate the models’ out-of-domain transfer and edition capabilities. We demonstrate the effectiveness of our approach against state-of-the-art 2D and 3D models using the AMOS abdominal organ segmentation dataset.