In this paper, a probabilistic and level set model for three-dimensional medical object extraction is proposed, which is called region competition based active contour. The algorithms are derived by minimizing a region based probabilistic energy function and implemented in a level set framework. An additional speed-controlling term makes the active contour quickly convergent to the actual contour on strong edges, whereas a probabilistic model makes the active contour performing well for weak edges. Prior knowledge about the initial contour and the probabilistic distribution contributes to more efficient extraction. The developed model has been applied to a variety of medical images, from CTA and MRA of the coronary to rotationally scanned and real-time three-dimensional echocardiography images of the mitral valve. As the results show, the algorithm is fast, convergent, adapted to a broad range of medical objects and produces satisfactory results.