Statistical models have shown considerable promise as a basis for segmenting and interpreting cardiac images. While a variety of statistical models have been proposed to improve the segmentation results, most of them are either static models (SMs), which neglect the temporal dynamics of a cardiac sequence, or generic dynamical models (GDMs), which are homogeneous in time and neglect the intersubject variability in cardiac shape and deformation. In this paper, we develop a subject-specific dynamical model (SSDM) that simultaneously handles temporal dynamics (intrasubject variability) and intersubject variability. We also propose a dynamic prediction algorithm that can progressively identify the specific motion patterns of a new cardiac sequence based on the shapes observed in past frames. The incorporation of this SSDM into the segmentation framework is formulated in a recursive Bayesian framework. It starts with a manual segmentation of the first frame, and then segments each frame according to intensity information from the current frame as well as the prediction from past frames. In addition, to reduce error propagation in sequential segmentation, we take into account the periodic nature of cardiac motion and perform segmentation in both forward and backward directions. We perform "leave-one-out" test on 32 canine sequences and 22 human sequences, and compare the experimental results with those from SM, GDM, and active appearance motion model (AAMM). Quantitative analysis of the experimental results shows that SSDM outperforms SM, GDM, and AAMM by having better global and local consistencies with manual segmentation. Moreover, we compare the segmentation results from forward and forward-backward segmentation. Quantitative evaluation shows that forward-backward segmentation suppresses the propagation of segmentation errors.