Nowadays, many NLP problems are tackled as supervised machine learning tasks. Consequently, the cost of the expertise needed to annotate the examples is a widespread issue. Active learning offers a framework to that issue, allowing to control the annotation cost while maximizing the classifier performance, but it relies on the key step of choosing which example will be proposed to the expert. In this paper, we examine and propose such selection strategies in the specific case of Conditional Random Fields (CRF) which are largely used in NLP. On the one hand, we propose a simple method to correct a bias of some state-of-the-art selection techniques. On the other hand, we detail an original approach to select the examples, based on the respect of proportions in the datasets. These contributions are validated over a large range of experiments implying several datasets and tasks, including named entity recognition, chunking, phonetization, word sense disambiguation.