Adaptive sparse signal estimation is needed for obtaining accurate channel knowledge in communication systems where the system response can be assumed to contain many near-zero coefficients. For sparse filter design, the zero-attracting LMS (ZA-LMS) incorporates the l1 norm penalty function into the quadratic LMS cost function to promote the sparseness during the adaptation process. The reweighted ZA-LMS (RZA-LMS) is developed using reweighted zero attractors with better performance. In this paper, we propose two new sparse LMS algorithms with segment zero attractors, referred as Segment RZA-LMS and Discrete Segment RZA-LMS. The Segment RZA-LMS outperforms RZA-LMS by using a piece-wise approximation of the reciprocal in the iterative algorithm of RZA-LMS. The Discrete Segment RZA-LMS is further developed to achieve faster convergence speed and lower steady state error performance than Segment RZA-LMS.