Abstract: This paper proposes a low-complexity receiver design that explicitly incorporates channel estimation uncertainties to address the performance degradation of underwater acoustic single-carrier frequency-domain equalization caused by channel estimation errors. The receiver models the estimated channel impulse response as a superposition of its true value and estimation error. By integrating sparse prior constraints into a statistical model framework, an expectation-maximization (EM) algorithm iteratively estimates the posterior distribution of the underwater acoustic channel while accounting for estimation inaccuracies. Subsequently, a minimum mean-square error frequency-domain decision feedback equalizer is analytically derived under this statistical framework to mitigate residual errors induced by channel estimation errors. Complexity analysis demonstrates that the proposed method bypasses matrix inversion operations during EM iterative processing and implements the decision feedback structure directly in the frequency domain, yielding markedly reduced computational complexity. Extensive numerical simulations and sea trials confirm that the proposed method achieves comparable or superior bit error rate performance relative to the conventional method, while significantly lowering computational latency in receiver implementation.