We present an adaptive translation quality estimation (QE) method to predict the human-targeted translation error rate (HTER) for a document-specific machine translation model. We first introduce features derived internal to the translation decoding process as well as externally from the source sentence analysis. We show the effectiveness of such features in both classification and regression of MT quality. By dynamically training the QE model for the document-specific MT model, we are able to achieve consistency and prediction quality across multiple documents, demonstrated by the higher correlation coefficient and F-scores in finding Good sentences. Additionally, the proposed method is applied to IBM English-to-Japanese MT post editing field study and we observe strong correlation with human preference, with a 10% increase in human translators' productivity. © 2014 Association for Computational Linguistics.