Event extraction involves the identification of instances of a type of event, along with their attributes and participants. Developing a training corpus by annotating events in text is very labor intensive, and so selecting informative instances to annotate can save a great deal of manual work. We present an active learning (AL) strategy, pseudo co-testing, based on one view from a classifier aiming to solve the original problem of event extraction, and another view from a classifier aiming to solve a coarser granularity task. As the second classifier can provide more graded matching from a wider scope, we can build a set of pseudo-contention-points which are very informative, and can speed up the AL process. Moreover, we incorporate multiple selection criteria into the pseudo co-testing, seeking training examples that are informative, representative, and varied. Experiments show that pseudo co-testing can reduce annotation labor by 81%; incorporating multiple selection criteria reduces the labor by a further 7%.