The high outage probability of an underwater wireless sensor may lead to high energy consumption. How to reduce the outage probability should be considered for underwater acoustic sensor networks (UASNs), where battery change is very difficult. Power control, which is one of the technologies to effectively reduce the outage probability, has also been developed for UASNs. However, when using the power control method with the maximum power to transmit packets, the slow fading of the signal in UASNs leads to serious accumulative interference in the receiver, which in turn leads to an even higher outage probability. Another challenge in UASNs is the complex acoustic channel condition with time-space-frequency variation and uncertain TL, which make it difficult to obtain the channel status information (CSI). To address these issues, a novel partial power control (PPC) algorithm based on outage probability minimization in UASNs is proposed. The proposed algorithm captures transmission loss (TL) using the Markov chain Monte Carlo (MCMC) method and estimates CSI in the next moment using AR prediction. The simulation results show that the proposed algorithm can effectively reduce the accumulative interference to the receiver and then reduce the outage probability by 19.3% at the maximum.