Underwater sensor networks (USNs) are essential for marine exploration and monitoring, yet their performance is limited by transmission reliability in complex underwater environments. Underwater acoustic (UWA) transmission offers a practical solution, with orthogonal frequency division multiplexing (OFDM) extensively adopted for its high data rate and multiple access capability. However, OFDM is extremely vulnerable to Doppler-induced distortions, which degrade demodulation performance. Conventional cross-ambiguity function (CAF) methods estimate Doppler through signal-level correlation, but achieving high accuracy requires long training sequences, thus incurring frame overhead and reducing effective data rates. To address this issue, a novel bit-level correlation (BLC) Doppler estimation algorithm enables accurate estimation with diminished training overhead. A tailored OFDM frame uses the first two OFDM symbols, modulated with M-sequences, as training sequences. Demodulated bits are correlated with a local M-sequence via vector inner-product computation, converting correlation from the signal level to the bit level. Numerical simulations under three Doppler scales demonstrate the effectiveness of the BLC algorithm. By correlating the demodulated training bits with a local M-sequence reference, the proposed algorithm provides a favorable trade-off between training overhead and estimation accuracy under the tested simulation conditions.