This paper proposes to use Word Confidence Estimation (WCE) information to improve MT outputs via N-best list re-ranking. From the confidence label assigned for each word in the MT hypothesis , we add six scores to the baseline log-linear model in order to re-rank the N-best list. Firstly, the correlation between the WCE-based sentence-level scores and the conventional evaluation scores (BLEU, TER, TERp-A) is investigated. Then, the N-best list re-ranking is evaluated over different WCE system performance levels: from our real and efficient WCE system (ranked 1st during last WMT 2013 Quality Estimation Task) to an oracle WCE (which simulates an interactive scenario where a user simply validates words of a MT hypothesis and the new output will be automatically regenerated). The results suggest that our real WCE system slightly (but significantly) improves the baseline while the oracle one extremely boosts it; and better WCE leads to better MT quality.